--- title: Our Framework for Reviewing AI-Generated Code | daily.dev description: JetBrains Research, with Lund University, presents a new study and conceptual framework for reviewing AI-generated code, arguing that the core challenge is... canonical: https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Our Framework for Reviewing AI-Generated Code | daily.dev og:description: JetBrains Research, with Lund University, presents a new study and conceptual framework for reviewing AI-generated code, arguing that the core challenge is... og:url: https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z og:image: https://api.daily.dev/og/posts/1pu6x7b7z.png og:image:alt: Our Framework for Reviewing AI-Generated Code og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Our Framework for Reviewing AI-Generated Code **[JetBrains](https://daily.dev/sources/jetbrains)** · 7 min read · 0 upvotes · 0 comments ## Summary JetBrains Research, with Lund University, presents a new study and conceptual framework for reviewing AI-generated code, arguing that the core challenge is 'trust calibration' rather than diffing. Since LLMs present every line with uniform confidence regardless of actual certainty, traditional line-by-line diff viewers fail to scale as AI agents generate larger changesets. Based on participatory design with 17 practitioners and a survey of 43 professionals, the paper proposes a three-level review workflow (overview, file-level risk stratification, then detailed snippet analysis) inspired by Shneiderman's visualization principle. The post also surveys existing tools like CodeRabbit, Claude Code, Graphite, and GitHub Copilot that partially address pieces of this workflow but none fully solve trust calibration, and outlines design guidelines for overview-, file-, and snippet-level review tools. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why do traditional diff viewers fail when reviewing AI-generated code? Diff viewers assume the reviewer's job is understanding what changed, but with AI-generated code understanding the change is easy while knowing where to trust it is hard. LLMs present every line with the same apparent confidence regardless of actual uncertainty, so there's no signal distinguishing straightforward logic from a risky stretch, making line-by-line diffing scale poorly as agents generate larger changesets. _daily.dev surfaces research like this for developers rethinking how they review large AI-generated diffs._ ### What is trust calibration in the context of reviewing AI-generated code? Trust calibration is the capacity to allocate review effort proportionate to segment-level risk when the code's author (an LLM) can't be asked about its confidence or reasoning. Human code review supports this implicitly through social cues like knowing a colleague's strengths, but AI-generated code review currently offers almost no equivalent support, forcing reviewers to either audit every line or fly blind. _Developers building AI-review habits can track emerging frameworks like this one on daily.dev._ ### What three-level workflow is proposed for reviewing AI-generated code changes? The proposed workflow moves from overview to files to code snippets: first the reviewer forms high-level hypotheses, then selectively drills into files with risk stratification applied before reading any line, then performs fine-grained analytical review of chunks with chain-of-thought linkage. It follows Shneiderman's visualization principle of overview first, then zoom and filter, then details on demand, and emerged from four workshops with 17 practitioners and a 43-person survey. _daily.dev helps engineers follow new proposals for structuring AI code review workflows._ ## Similar posts on daily.dev - [AI Code Review Best Practices](https://daily.dev/posts/ai-code-review-best-practices-ii7hbaa3j) · Aviator Blog · 15 upvotes · 1 comments - [Code Review Has Changed. The Contract Needs to Change Too.](https://daily.dev/posts/code-review-has-changed-the-contract-needs-to-change-too--9t5wi90oo) · Kilo Blog · 1 upvotes · 0 comments - [Code Reviews in the Age of AI](https://daily.dev/posts/code-reviews-in-the-age-of-ai-acvy63drb) · Earthly · 1 upvotes · 0 comments --- Tags: [#devtools](https://daily.dev/tags/devtools), [#ai-coding](https://daily.dev/tags/ai-coding), [#code-review](https://daily.dev/tags/code-review), [#jetbrains](https://daily.dev/tags/jetbrains) [View this post on daily.dev](https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Our Framework for Reviewing AI-Generated Code","url":"https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z"},"datePublished":"2026-10-06T14:09:04.501Z","dateModified":"2026-10-07T03:55:18.789Z","description":"JetBrains Research, with Lund University, presents a new study and conceptual framework for reviewing AI-generated code, arguing that the core challenge is...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4007b3c67bbcac8e2e717cf29188c64b?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/4007b3c67bbcac8e2e717cf29188c64b?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"JetBrains","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"JetBrains","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/53ecf0c676f34b3896ee109609d91efa","url":"https://daily.dev/sources/jetbrains"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"devtools,ai-coding,code-review,jetbrains","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"JetBrains","item":"https://daily.dev/sources/jetbrains"},{"@type":"ListItem","position":3,"name":"Our Framework for Reviewing AI-Generated Code"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/our-framework-for-reviewing-ai-generated-code-1pu6x7b7z#faq","mainEntity":[{"@type":"Question","name":"Why do traditional diff viewers fail when reviewing AI-generated code?","acceptedAnswer":{"@type":"Answer","text":"Diff viewers assume the reviewer's job is understanding what changed, but with AI-generated code understanding the change is easy while knowing where to trust it is hard. 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